code: def get_proba(cov_00, cov_01, cov_11, clf):
      cov = np.array([[cov_00, cov_01], [cov_01, cov_11]])
      with np.testing.suppress_warnings() as sup:
          sup.filter(RuntimeWarning)
          return clf.predict_proba(cov[np.newaxis, ...])[0, 1]
  
file_path: pyRiemann/examples/simulated/plot_classifier_comparison.py
imports: from functools import partial
  from time import time
  import numpy
  import matplotlib.pyplot
  from matplotlib.colors import ListedColormap
  from sklearn.model_selection import train_test_split
  from pyriemann.datasets import make_covariances, make_gaussian_blobs
  from pyriemann.classification import MDM, KNearestNeighbor, SVC, MeanField
label_desc: Use image node_id 1 for calling a global function with example usage: get_proba(cov_00, cov_01, cov_11, clf) and returns: clf